Can Google Data Help Predict French Youth Unemployment?

Frédéric Karamé and Yannick Fondeur ()
Additional contact information
Yannick Fondeur: Centre d’Etudes de l’Emploi, https://ceet.cnam.fr/

No 12-03, Documents de recherche from Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne

Abstract: According to the rising “Google econometrics” literature, Google queries may help predict economic activity. The aim of our paper is to test if these data can enhance predictions for youth unemployment in France. As we have on the one hand weekly series on web search queries and on the other hand monthly series on unemployment for the 15 to 24-year-olds, we use the unobserved components approach in order to exploit all available information. Our model is estimated with a modified version of the Kalman filter taking into account the twofold issues of non-stationarity and multiple frequencies in our data. We find that including Google data improves unemployment predictions relatively to a competing model without search data queries.

Keywords: Google econometrics; forecasting; nowcasting; unemployment; unobserved components; diffuse initialization; Kalman filter; univariate treatment of time series; smoothing; multivariate models (search for similar items in EconPapers)
JEL-codes: C22 C51 E32 E37 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2012
New Economics Papers: this item is included in nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Can Google data help predict French youth unemployment? (2013)
Working Paper: Can Google data help predict French youth unemployment? (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:eve:wpaper:12-03

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